Abstract: A document-oriented search can be executed to generate a set of document results, at least one of the documents associated with at least one potential expert. The document results can be analyzed to produce a list of potential experts. An expertise score for at least one of the potential experts can be calculated based on a content score and a metadata score for the at least one of the potential experts. A confidence score for the potential expert can be calculated based on a diversity-constrained content score and a diversity-constrained metadata score for the at least one of the potential experts, the diversity-constrained content score and the diversity-constrained metadata score calculated using an evidence diversity score for the at least one of the potential experts. A list of experts with associated confidence scores that are above a confidence score threshold can be sent to a client device.

Abstract: A document-oriented search can be executed to generate a set of document results, at least one of the documents associated with at least one potential expert. The document results can be analyzed to produce a list of potential experts. An expertise score for at least one of the potential experts can be calculated based on a content score and a metadata score for the at least one of the potential experts. A confidence score for the potential expert can be calculated based on a diversity-constrained content score and a diversity-constrained metadata score for the at least one of the potential experts, the diversity-constrained content score and the diversity-constrained metadata score calculated using an evidence diversity score for the at least one of the potential experts. A list of experts with associated confidence scores that are above a confidence score threshold can be sent to a client device.

Abstract: A document-oriented search can be executed to generate a set of document results, at least one of the documents associated with at least one potential expert. The document results can be analyzed to produce a list of potential experts. An expertise score for at least one of the potential experts can be calculated based on a content score and a metadata score for the at least one of the potential experts. A confidence score for the potential expert can be calculated based on a diversity-constrained content score and a diversity-constrained metadata score for the at least one of the potential experts, the diversity-constrained content score and the diversity-constrained metadata score calculated using an evidence diversity score for the at least one of the potential experts. A list of experts with associated confidence scores that are above a confidence score threshold can be sent to a client device.

Abstract: A method includes executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document associated with at least one potential expert. The method includes analyzing the document results to produce a list of potential experts. The method includes calculating an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert. The method includes calculating an evidence diversity score for each potential expert. The method includes calculating a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert. The method includes displaying a list of potential experts with associated confidence scores.

Abstract: A document-oriented search can be executed to generate a set of document results, at least one of the documents associated with at least one potential expert. The document results can be analyzed to produce a list of potential experts. An expertise score for at least one of the potential experts can be calculated based on a content score and a metadata score for the at least one of the potential experts. A confidence score for the potential expert can be calculated based on a diversity-constrained content score and a diversity-constrained metadata score for the at least one of the potential experts, the diversity-constrained content score and the diversity-constrained metadata score calculated using an evidence diversity score for the at least one of the potential experts. A list of experts with associated confidence scores that are above a confidence score threshold can be sent to a client device.

Abstract: Software that uses data collected from queries performed on a graph database to dynamically improve graph partitioning. The software performs the following operations: (i) identifying a partitioned graph database including a set of edges and a set of vertices, wherein each vertex of the set of vertices is associated with one or more edges of the set of edges; (ii) determining an edge traversal value for one or more edges of the set of edges, wherein the edge traversal value for a respective edge relates to a number of times that the respective edge is traversed in response to one or more queries of the graph database; and (iii) calculating a first vertex score for a first vertex on a first graph partition, based, at least in part, on the edge traversal value(s) for one or more edges associated with the first vertex.

Abstract: A method includes executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document associated with at least one potential expert. The method includes analyzing the document results to produce a list of potential experts. The method includes calculating an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert. The method includes calculating an evidence diversity score for each potential expert. The method includes calculating a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert. The method includes displaying a list of potential experts with associated confidence scores.

Abstract: Software that uses data collected from queries performed on a graph database to dynamically improve graph partitioning. The software performs the following operations: (i) identifying a partitioned graph database including a set of edges and a set of vertices, wherein each vertex of the set of vertices is associated with one or more edges of the set of edges; (ii) determining an edge traversal value for one or more edges of the set of edges, wherein the edge traversal value for a respective edge relates to a number of times that the respective edge is traversed in response to one or more queries of the graph database; and (iii) calculating a first vertex score for a first vertex on a first graph partition, based, at least in part, on the edge traversal value(s) for one or more edges associated with the first vertex.

Abstract: Examples of techniques for calculating confidence scores are described herein. A method includes executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document associated with at least one potential expert. The method includes analyzing the document results to produce a list of potential experts. The method includes calculating an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert. The method includes calculating an evidence diversity score for each potential expert. The method includes calculating a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert. The method includes displaying a list of potential experts with associated confidence scores.

Abstract: Examples of techniques for calculating confidence scores are described herein. A method includes executing, via a processor, a document-oriented search based on a query in an index of documents to generate a set of document results, each document associated with at least one potential expert. The method includes analyzing the document results to produce a list of potential experts. The method includes calculating an expertise score for each potential expert based on a calculated content score and metadata score for each potential expert. The method includes calculating an evidence diversity score for each potential expert. The method includes calculating a confidence score for each potential expert based on a diversity-constrained content score and a diversity-constrained metadata score for each potential expert. The method includes displaying a list of potential experts with associated confidence scores.